MueLu_RepartitionFactory_def.hpp

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#ifndef MUELU_REPARTITIONFACTORY_DEF_HPP
#define MUELU_REPARTITIONFACTORY_DEF_HPP

#include <algorithm>
#include <iostream>
#include <sstream>

#include "MueLu_RepartitionFactory_decl.hpp" // TMP JG NOTE: before other includes, otherwise I cannot test the fwd declaration in _def

#ifdef HAVE_MPI
#include <Teuchos_DefaultMpiComm.hpp>
#include <Teuchos_CommHelpers.hpp>

#include <Xpetra_Map.hpp>
#include <Xpetra_MapFactory.hpp>
#include <Xpetra_MultiVectorFactory.hpp>
#include <Xpetra_VectorFactory.hpp>
#include <Xpetra_Import.hpp>
#include <Xpetra_ImportFactory.hpp>
#include <Xpetra_Export.hpp>
#include <Xpetra_ExportFactory.hpp>
#include <Xpetra_Matrix.hpp>
#include <Xpetra_MatrixFactory.hpp>

#include "MueLu_Utilities.hpp"

#include "MueLu_CloneRepartitionInterface.hpp"

#include "MueLu_Level.hpp"
#include "MueLu_MasterList.hpp"
#include "MueLu_Monitor.hpp"

namespace MueLu {

 template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
 RCP<const ParameterList> RepartitionFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::GetValidParameterList() const {
    RCP<ParameterList> validParamList = rcp(new ParameterList());

#define SET_VALID_ENTRY(name) validParamList->setEntry(name, MasterList::getEntry(name))
    SET_VALID_ENTRY("repartition: print partition distribution");
    SET_VALID_ENTRY("repartition: remap parts");
    SET_VALID_ENTRY("repartition: remap num values");
#undef  SET_VALID_ENTRY

    validParamList->set< RCP<const FactoryBase> >("A",                    Teuchos::null, "Factory of the matrix A");
    validParamList->set< RCP<const FactoryBase> >("number of partitions", Teuchos::null, "Instance of RepartitionHeuristicFactory.");
    validParamList->set< RCP<const FactoryBase> >("Partition",            Teuchos::null, "Factory of the partition");

    return validParamList;
  }

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void RepartitionFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::DeclareInput(Level &currentLevel) const {
    Input(currentLevel, "A");
    Input(currentLevel, "number of partitions");
    Input(currentLevel, "Partition");
  }

  template<class T> class MpiTypeTraits            { public: static MPI_Datatype getType(); };
  template<>        class MpiTypeTraits<long>      { public: static MPI_Datatype getType() { return MPI_LONG;      } };
  template<>        class MpiTypeTraits<int>       { public: static MPI_Datatype getType() { return MPI_INT;       } };
  template<>        class MpiTypeTraits<short>     { public: static MPI_Datatype getType() { return MPI_SHORT;     } };
  template<>        class MpiTypeTraits<unsigned>  { public: static MPI_Datatype getType() { return MPI_UNSIGNED;  } };
  template<>        class MpiTypeTraits<long long> { public: static MPI_Datatype getType() { return MPI_LONG_LONG; } };

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void RepartitionFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Build(Level& currentLevel) const {
    FactoryMonitor m(*this, "Build", currentLevel);

    const Teuchos::ParameterList & pL = GetParameterList();
    // Access parameters here to make sure that we set the parameter entry flag to "used" even in case of short-circuit evaluation.
    // TODO (JG): I don't really know if we want to do this.
    bool   remapPartitions     = pL.get<bool>  ("repartition: remap parts");

    // TODO: We only need a CrsGraph. This class does not have to be templated on Scalar types.
    RCP<Matrix> A = Get< RCP<Matrix> >(currentLevel, "A");
    RCP<const Map>            rowMap = A->getRowMap();
    GO                     indexBase = rowMap->getIndexBase();
    Xpetra::UnderlyingLib  lib       = rowMap->lib();

    // NOTE: Teuchos::MPIComm::duplicate() calls MPI_Bcast inside, so this is
    // a synchronization point. However, as we do MueLu_sumAll afterwards anyway, it
    // does not matter.
    RCP<const Teuchos::Comm<int> > origComm = rowMap->getComm();
    RCP<const Teuchos::Comm<int> > comm     = origComm->duplicate();

    int myRank   = comm->getRank();
    int numProcs = comm->getSize();

    RCP<const Teuchos::MpiComm<int> > tmpic = rcp_dynamic_cast<const Teuchos::MpiComm<int> >(comm);
    TEUCHOS_TEST_FOR_EXCEPTION(tmpic == Teuchos::null, Exceptions::RuntimeError, "Cannot cast base Teuchos::Comm to Teuchos::MpiComm object.");
    RCP<const Teuchos::OpaqueWrapper<MPI_Comm> > rawMpiComm = tmpic->getRawMpiComm();

    int numPartitions = Get<int>(currentLevel, "number of partitions");

    // ======================================================================================================
    // Construct decomposition vector
    // ======================================================================================================
    RCP<GOVector> decomposition = Get<RCP<GOVector> >(currentLevel, "Partition");

    // check which factory provides "Partition"
    if(remapPartitions == true && Teuchos::rcp_dynamic_cast<const CloneRepartitionInterface>(GetFactory("Partition")) != Teuchos::null) {
      // if "Partition" is provided by a CloneRepartitionInterface class we have to switch of remapPartitions
      // as we can assume the processor Ids in Partition to be the expected ones. If we would do remapping we
      // would get different processors for the different blocks which screws up matrix-matrix multiplication.
      remapPartitions = false;
    }

    // check special cases
    if (numPartitions == 1) {
      // Trivial case: decomposition is the trivial one, all zeros. We skip the call to Zoltan_Interface
      // (this is mostly done to avoid extra output messages, as even if we didn't skip there is a shortcut
      // in Zoltan[12]Interface).
      // TODO: We can probably skip more work in this case (like building all extra data structures)
      GetOStream(Warnings0) << "Only one partition: Skip call to the repartitioner." << std::endl;
    } else if (numPartitions == -1) {
      // No repartitioning necessary: decomposition should be Teuchos::null
      GetOStream(Warnings0) << "No repartitioning necessary: partitions were left unchanged by the repartitioner" << std::endl;
      Set<RCP<const Import> >(currentLevel, "Importer", Teuchos::null);
      return;
    }

    // ======================================================================================================
    // Remap if necessary
    // ======================================================================================================
    // From a user perspective, we want user to not care about remapping, thinking of it as only a performance feature.
    // There are two problems, however.
    // (1) Next level aggregation depends on the order of GIDs in the vector, if one uses "natural" or "random" orderings.
    //     This also means that remapping affects next level aggregation, despite the fact that the _set_ of GIDs for
    //     each partition is the same.
    // (2) Even with the fixed order of GIDs, the remapping may influence the aggregation for the next-next level.
    //     Let us consider the following example. Lets assume that when we don't do remapping, processor 0 would have
    //     GIDs {0,1,2}, and processor 1 GIDs {3,4,5}, and if we do remapping processor 0 would contain {3,4,5} and
    //     processor 1 {0,1,2}. Now, when we run repartitioning algorithm on the next level (say Zoltan1 RCB), it may
    //     be dependent on whether whether it is [{0,1,2}, {3,4,5}] or [{3,4,5}, {0,1,2}]. Specifically, the tie-breaking
    //     algorithm can resolve these differently. For instance, running
    //         mpirun -np 5 ./MueLu_ScalingTestParamList.exe --xml=easy_sa.xml --nx=12 --ny=12 --nz=12
    //     with
    //         <ParameterList name="MueLu">
    //           <Parameter name="coarse: max size"                type="int"      value="1"/>
    //           <Parameter name="repartition: enable"             type="bool"     value="true"/>
    //           <Parameter name="repartition: min rows per proc"  type="int"      value="2"/>
    //           <ParameterList name="level 1">
    //             <Parameter name="repartition: remap parts"      type="bool"     value="false/true"/>
    //           </ParameterList>
    //         </ParameterList>
    //     produces different repartitioning for level 2.
    //     This different repartitioning may then escalate into different aggregation for the next level.
    //
    // We fix (1) by fixing the order of GIDs in a vector by sorting the resulting vector.
    // Fixing (2) is more complicated.
    // FIXME: Fixing (2) in Zoltan may not be enough, as we may use some arbitration in MueLu,
    // for instance with CoupledAggregation. What we really need to do is to use the same order of processors containing
    // the same order of GIDs. To achieve that, the newly created subcommunicator must be conforming with the order. For
    // instance, if we have [{0,1,2}, {3,4,5}], we create a subcommunicator where processor 0 gets rank 0, and processor 1
    // gets rank 1. If, on the other hand, we have [{3,4,5}, {0,1,2}], we assign rank 1 to processor 0, and rank 0 to processor 1.
    // This rank permutation requires help from Epetra/Tpetra, both of which have no such API in place.
    // One should also be concerned that if we had such API in place, rank 0 in subcommunicator may no longer be rank 0 in
    // MPI_COMM_WORLD, which may lead to issues for logging.
    if (remapPartitions) {
      SubFactoryMonitor m1(*this, "DeterminePartitionPlacement", currentLevel);

      DeterminePartitionPlacement(*A, *decomposition, numPartitions);
    }

    // ======================================================================================================
    // Construct importer
    // ======================================================================================================
    // At this point, the following is true:
    //  * Each processors owns 0 or 1 partitions
    //  * If a processor owns a partition, that partition number is equal to the processor rank
    //  * The decomposition vector contains the partitions ids that the corresponding GID belongs to

    ArrayRCP<const GO> decompEntries;
    if (decomposition->getLocalLength() > 0)
      decompEntries = decomposition->getData(0);

#ifdef HAVE_MUELU_DEBUG
    // Test range of partition ids
    int incorrectRank = -1;
    for (int i = 0; i < decompEntries.size(); i++)
      if (decompEntries[i] >= numProcs || decompEntries[i] < 0) {
        incorrectRank = myRank;
        break;
      }

    int incorrectGlobalRank = -1;
    MueLu_maxAll(comm, incorrectRank, incorrectGlobalRank);
    TEUCHOS_TEST_FOR_EXCEPTION(incorrectGlobalRank >- 1, Exceptions::RuntimeError, "pid " + Teuchos::toString(incorrectGlobalRank) + " encountered a partition number is that out-of-range");
#endif

    Array<GO> myGIDs;
    myGIDs.reserve(decomposition->getLocalLength());

    // Step 0: Construct mapping
    //    part number -> GIDs I own which belong to this part
    // NOTE: my own part GIDs are not part of the map
    typedef std::map<GO, Array<GO> > map_type;
    map_type sendMap;
    for (LO i = 0; i < decompEntries.size(); i++) {
      GO id  = decompEntries[i];
      GO GID = rowMap->getGlobalElement(i);

      if (id == myRank)
        myGIDs     .push_back(GID);
      else
        sendMap[id].push_back(GID);
    }
    decompEntries = Teuchos::null;

    if (IsPrint(Statistics2)) {
      GO numLocalKept = myGIDs.size(), numGlobalKept, numGlobalRows = A->getGlobalNumRows();
      MueLu_sumAll(comm,numLocalKept, numGlobalKept);
      GetOStream(Statistics2) << "Unmoved rows: " << numGlobalKept << " / " << numGlobalRows << " (" << 100*Teuchos::as<double>(numGlobalKept)/numGlobalRows << "%)" << std::endl;
    }

    int numSend = sendMap.size(), numRecv;

    // Arrayify map keys
    Array<GO> myParts(numSend), myPart(1);
    int cnt = 0;
    myPart[0] = myRank;
    for (typename map_type::const_iterator it = sendMap.begin(); it != sendMap.end(); it++)
      myParts[cnt++] = it->first;

    // Step 1: Find out how many processors send me data
    // partsIndexBase starts from zero, as the processors ids start from zero
    GO partsIndexBase = 0;
    RCP<Map>    partsIHave  = MapFactory   ::Build(lib, Teuchos::OrdinalTraits<Xpetra::global_size_t>::invalid(), myParts(), partsIndexBase, comm);
    RCP<Map>    partsIOwn   = MapFactory   ::Build(lib,                                                 numProcs,  myPart(), partsIndexBase, comm);
    RCP<Export> partsExport = ExportFactory::Build(partsIHave, partsIOwn);

    RCP<GOVector> partsISend    = Xpetra::VectorFactory<GO, LO, GO, NO>::Build(partsIHave);
    RCP<GOVector> numPartsIRecv = Xpetra::VectorFactory<GO, LO, GO, NO>::Build(partsIOwn);
    if (numSend) {
      ArrayRCP<GO> partsISendData = partsISend->getDataNonConst(0);
      for (int i = 0; i < numSend; i++)
        partsISendData[i] = 1;
    }
    (numPartsIRecv->getDataNonConst(0))[0] = 0;

    numPartsIRecv->doExport(*partsISend, *partsExport, Xpetra::ADD);
    numRecv = (numPartsIRecv->getData(0))[0];

    // Step 2: Get my GIDs from everybody else
    MPI_Datatype MpiType = MpiTypeTraits<GO>::getType();
    int msgTag = 12345;  // TODO: use Comm::dup for all internal messaging

    // Post sends
    Array<MPI_Request> sendReqs(numSend);
    cnt = 0;
    for (typename map_type::iterator it = sendMap.begin(); it != sendMap.end(); it++)
      MPI_Isend(static_cast<void*>(it->second.getRawPtr()), it->second.size(), MpiType, Teuchos::as<GO>(it->first), msgTag, *rawMpiComm, &sendReqs[cnt++]);

    map_type recvMap;
    size_t totalGIDs = myGIDs.size();
    for (int i = 0; i < numRecv; i++) {
      MPI_Status status;
      MPI_Probe(MPI_ANY_SOURCE, msgTag, *rawMpiComm, &status);

      // Get rank and number of elements from status
      int fromRank = status.MPI_SOURCE, count;
      MPI_Get_count(&status, MpiType, &count);

      recvMap[fromRank].resize(count);
      MPI_Recv(static_cast<void*>(recvMap[fromRank].getRawPtr()), count, MpiType, fromRank, msgTag, *rawMpiComm, &status);

      totalGIDs += count;
    }

    // Do waits on send requests
    if (numSend) {
      Array<MPI_Status> sendStatuses(numSend);
      MPI_Waitall(numSend, sendReqs.getRawPtr(), sendStatuses.getRawPtr());
    }

    // Merge GIDs
    myGIDs.reserve(totalGIDs);
    for (typename map_type::const_iterator it = recvMap.begin(); it != recvMap.end(); it++) {
      int offset = myGIDs.size(), len = it->second.size();
      if (len) {
        myGIDs.resize(offset + len);
        memcpy(myGIDs.getRawPtr() + offset, it->second.getRawPtr(), len*sizeof(GO));
      }
    }
    // NOTE 2: The general sorting algorithm could be sped up by using the knowledge that original myGIDs and all received chunks
    // (i.e. it->second) are sorted. Therefore, a merge sort would work well in this situation.
    std::sort(myGIDs.begin(), myGIDs.end());

    // Step 3: Construct importer
    RCP<Map>          newRowMap      = MapFactory   ::Build(lib, rowMap->getGlobalNumElements(), myGIDs(), indexBase, origComm);
    RCP<const Import> rowMapImporter;
    {
      SubFactoryMonitor m1(*this, "Import construction", currentLevel);
      rowMapImporter = ImportFactory::Build(rowMap, newRowMap);
    }

    Set(currentLevel, "Importer", rowMapImporter);

    // ======================================================================================================
    // Print some data
    // ======================================================================================================
    if (pL.get<bool>("repartition: print partition distribution") && IsPrint(Statistics2)) {
      // Print the grid of processors
      GetOStream(Statistics2) << "Partition distribution over cores (ownership is indicated by '+')" << std::endl;

      char amActive = (myGIDs.size() ? 1 : 0);
      std::vector<char> areActive(numProcs, 0);
      MPI_Gather(&amActive, 1, MPI_CHAR, &areActive[0], 1, MPI_CHAR, 0, *rawMpiComm);

      int rowWidth = std::min(Teuchos::as<int>(ceil(sqrt(numProcs))), 100);
      for (int proc = 0; proc < numProcs; proc += rowWidth) {
        for (int j = 0; j < rowWidth; j++)
          if (proc + j < numProcs)
            GetOStream(Statistics2) << (areActive[proc + j] ? "+" : ".");
          else
          GetOStream(Statistics2) << " ";

        GetOStream(Statistics2) << "      " << proc << ":" << std::min(proc + rowWidth, numProcs) - 1 << std::endl;
      }
    }

  } // Build

  //----------------------------------------------------------------------
  template<typename T, typename W>
  struct Triplet {
    T    i, j;
    W    v;
  };
  template<typename T, typename W>
  static bool compareTriplets(const Triplet<T,W>& a, const Triplet<T,W>& b) {
    return (a.v > b.v); // descending order
  }

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void RepartitionFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::
  DeterminePartitionPlacement(const Matrix& A, GOVector& decomposition, GO numPartitions) const {
    RCP<const Map> rowMap = A.getRowMap();

    RCP<const Teuchos::Comm<int> > comm = rowMap->getComm()->duplicate();
    int numProcs = comm->getSize();

    RCP<const Teuchos::MpiComm<int> > tmpic = rcp_dynamic_cast<const Teuchos::MpiComm<int> >(comm);
    TEUCHOS_TEST_FOR_EXCEPTION(tmpic == Teuchos::null, Exceptions::RuntimeError, "Cannot cast base Teuchos::Comm to Teuchos::MpiComm object.");
    RCP<const Teuchos::OpaqueWrapper<MPI_Comm> > rawMpiComm = tmpic->getRawMpiComm();

    const Teuchos::ParameterList& pL = GetParameterList();

    // maxLocal is a constant which determins the number of largest edges which are being exchanged
    // The idea is that we do not want to construct the full bipartite graph, but simply a subset of
    // it, which requires less communication. By selecting largest local edges we hope to achieve
    // similar results but at a lower cost.
    const int maxLocal = pL.get<int>("repartition: remap num values");
    const int dataSize = 2*maxLocal;

    ArrayRCP<GO> decompEntries;
    if (decomposition.getLocalLength() > 0)
      decompEntries = decomposition.getDataNonConst(0);

    // Step 1: Sort local edges by weight
    // Each edge of a bipartite graph corresponds to a triplet (i, j, v) where
    //   i: processor id that has some piece of part with part_id = j
    //   j: part id
    //   v: weight of the edge
    // We set edge weights to be the total number of nonzeros in rows on this processor which
    // correspond to this part_id. The idea is that when we redistribute matrix, this weight
    // is a good approximation of the amount of data to move.
    // We use two maps, original which maps a partition id of an edge to the corresponding weight,
    // and a reverse one, which is necessary to sort by edges.
    std::map<GO,GO> lEdges;
    for (LO i = 0; i < decompEntries.size(); i++)
      lEdges[decompEntries[i]] += A.getNumEntriesInLocalRow(i);

    // Reverse map, so that edges are sorted by weight.
    // This results in multimap, as we may have edges with the same weight
    std::multimap<GO,GO> revlEdges;
    for (typename std::map<GO,GO>::const_iterator it = lEdges.begin(); it != lEdges.end(); it++)
      revlEdges.insert(std::make_pair(it->second, it->first));

    // Both lData and gData are arrays of data which we communicate. The data is stored
    // in pairs, so that data[2*i+0] is the part index, and data[2*i+1] is the corresponding edge weight.
    // We do not store processor id in data, as we can compute that by looking on the offset in the gData.
    Array<GO> lData(dataSize, -1), gData(numProcs * dataSize);
    int numEdges = 0;
    for (typename std::multimap<GO,GO>::reverse_iterator rit = revlEdges.rbegin(); rit != revlEdges.rend() && numEdges < maxLocal; rit++) {
      lData[2*numEdges+0] = rit->second; // part id
      lData[2*numEdges+1] = rit->first;  // edge weight
      numEdges++;
    }

    // Step 2: Gather most edges
    // Each processors contributes maxLocal edges by providing maxLocal pairs <part id, weight>, which is of size dataSize
    MPI_Datatype MpiType = MpiTypeTraits<GO>::getType();
    MPI_Allgather(static_cast<void*>(lData.getRawPtr()), dataSize, MpiType, static_cast<void*>(gData.getRawPtr()), dataSize, MpiType, *rawMpiComm);

    // Step 3: Construct mapping

    // Construct the set of triplets
    std::vector<Triplet<int,int> > gEdges(numProcs * maxLocal);
    size_t k = 0;
    for (LO i = 0; i < gData.size(); i += 2) {
      GO part   = gData[i+0];
      GO weight = gData[i+1];
      if (part != -1) {                     // skip nonexistent edges
        gEdges[k].i = i/dataSize;           // determine the processor by its offset (since every processor sends the same amount)
        gEdges[k].j = part;
        gEdges[k].v = weight;
        k++;
      }
    }
    gEdges.resize(k);

    // Sort edges by weight
    // NOTE: compareTriplets is actually a reverse sort, so the edges weight is in decreasing order
    std::sort(gEdges.begin(), gEdges.end(), compareTriplets<int,int>);

    // Do matching
    std::map<int,int> match;
    std::vector<char> matchedRanks(numProcs, 0), matchedParts(numProcs, 0);
    int numMatched = 0;
    for (typename std::vector<Triplet<int,int> >::const_iterator it = gEdges.begin(); it != gEdges.end(); it++) {
      GO rank = it->i;
      GO part = it->j;
      if (matchedRanks[rank] == 0 && matchedParts[part] == 0) {
        matchedRanks[rank] = 1;
        matchedParts[part] = 1;
        match[part] = rank;
        numMatched++;
      }
    }
    GetOStream(Statistics1) << "Number of unassigned paritions before cleanup stage: " << (numPartitions - numMatched) << " / " << numPartitions << std::endl;

    // Step 4: Assign unassigned partitions
    // We do that through random matching for remaining partitions. Not all part numbers are valid, but valid parts are a subset of [0, numProcs).
    // The reason it is done this way is that we don't need any extra communication, as we don't need to know which parts are valid.
    for (int part = 0, matcher = 0; part < numProcs; part++)
      if (match.count(part) == 0) {
        // Find first non-matched rank
        while (matchedRanks[matcher])
          matcher++;

        match[part] = matcher++;
      }

    // Step 5: Permute entries in the decomposition vector
    for (LO i = 0; i < decompEntries.size(); i++)
      decompEntries[i] = match[decompEntries[i]];
  }

} // namespace MueLu

#endif //ifdef HAVE_MPI

#endif // MUELU_REPARTITIONFACTORY_DEF_HPP